Phase 1: Inventory and Dependency Mapping
Before you move anything, catalog what exists. List every table, view, stored procedure, and scheduled job in the current warehouse. For each item, document who owns it, how often it runs, what upstream source feeds it, and which downstream systems consume its output. This inventory is the foundation of the entire migration — skip it and you will discover orphaned dependencies during cutover when fixing them is expensive and urgent.
Map the dependency chain end to end: which dashboards read from which tables, which pipelines feed which views, and which external systems pull data through API endpoints or scheduled exports. Pay special attention to cross-team dependencies. A table your team considers low priority may be the source of truth for another department's quarterly board report.
The inventory phase typically takes one to two weeks depending on warehouse size and documentation quality. The time invested pays back many times over in avoided surprises during later phases. If documentation is sparse, interview the primary analysts and pipeline owners directly — their working knowledge often exceeds what the metadata catalog records.
Phase 2: Schema and Pipeline Translation
Schema translation is where platform differences surface. Column types, default values, constraint syntax, null handling, and partitioning strategies all vary between warehouse platforms. Build a type-mapping reference early and apply it consistently across every table migration. Automated schema conversion tools can accelerate this step, but always review the output manually — edge cases in data type precision, timestamp formatting, and character encoding are common sources of silent errors that only appear when production queries return unexpected results.
Pipeline translation covers the ETL or ELT jobs that feed the warehouse. Each job needs to be pointed at the new target, tested with sample data, and validated against expected row counts and checksums. If you are also changing your pipeline orchestration tooling during the migration, separate the two changes: migrate pipelines to the new target first using the existing orchestrator, stabilize the data flow, and then swap the orchestration layer in a subsequent phase.
Changing both the warehouse and the orchestrator at once doubles the debugging surface. When something breaks — and something always breaks — you need to know whether the problem is in the new platform, the new pipeline logic, or the new orchestrator. Isolating variables makes diagnosis possible.
Phase 3: Parallel Run and Validation
Run both the old and new warehouses in parallel for at least two full reporting cycles. During this window, pipelines feed both systems, and every scheduled report runs against both targets. Compare outputs systematically: row counts, aggregate totals, and a sample of detailed records should match within acceptable tolerance. Discrepancies caught during parallel running are cheap to fix. Discrepancies caught after cutover are expensive and erode organizational trust in the data.
Assign validation owners by domain. The finance team validates financial tables, the marketing team validates attribution tables, the operations team validates logistics data, and so on. Each owner signs off before you proceed to cutover. Spreading validation responsibility ensures that the people who actually use the data are the ones confirming it is correct rather than a central team that may not understand the business context of each table.
Use the cost worksheet on the home page to model the additional spend during the parallel period — running two systems simultaneously increases your infrastructure bill, and forecasting that cost prevents budget surprises. The parallel phase is temporary but essential, and its cost should be treated as an investment in migration quality rather than an expense to minimize.
Phase 4: Cutover and Decommission
Cutover is the point where the new warehouse becomes the system of record. Redirect all pipelines, dashboards, API consumers, and scheduled exports to the new system. Execute the cutover during a low-activity window to minimize disruption, and communicate the timeline to every stakeholder at least one week in advance so nobody is caught off guard by changed connection strings or query behavior differences.
After cutover, keep the old warehouse in read-only mode for at least thirty days. Consumers who missed the migration communication, automated scripts with hardcoded connection strings, or quarterly processes that only run once per period will surface during this grace period. Each incident that surfaces is a dependency the inventory missed — fix the consumer and update the inventory documentation.
Once no queries have hit the old system for a sustained period and you are confident all consumers have migrated, decommission the old warehouse to stop paying for idle resources. Document the entire migration end-to-end in a runbook — platform choices, schema mappings, validation results, and lessons learned — so the next migration starts from a known playbook rather than from scratch.
Every migration has platform-specific nuances — use this checklist as a framework and supplement it with your target platform's official migration documentation.